Long-Lasting Tumor Response in Patients with Panitumumab Monotherapy for Chemorefractory Metastatic Colorectal Carcinoma – A Report of Two Cases

Background: Second as well as higher-line therapies have a significant influence on progression-free and overall survival of metastatic colorectal cancer patients. However, treatment of late-stage disease remains suboptimal. Therefore, the introduction of new, effective and well-tolerated agents is of major importance. Case Reports: Here we describe the cases of 2 patients with metastatic KRAS wild-type colorectal cancer who received a fourth-line monotherapy with panitumumab after failure of 5-fluorouracil, irinotecan, oxaliplatin, and bevacizumab. Results: Both patients achieved a partial remission, and for 11.5 and 18 months, respectively, they had a stable disease with initial reduction in the tumor marker carcinoembryonic antigen. Both patients reported a good tolerability of the treatment with improved quality of life (compared to receiving combined chemotherapy). Conclusion: Panitumumab monotherapy is an effective and well tolerated treatment of metastatic colorectal cancer in extensively pretreated KRAS wild-type patients. Our data have shown a response to panitumumab monotherapy for more than 11 months

Unsupervised Learning of Spatio-Temporal Receptive Fields from an Event-Based Vision Sensor

International audienceNeuromorphic vision sensors exhibit several advantages compared to conventional frame-based cameras including low latencies, high dynamic range, and low data rates. However, how efficient visual representations can be learned from the output of such sensors in an unsupervised fashion is still an open problem. Here we present a spiking neural network that learns spatio-temporal receptive fields in an unsupervised way from the output of a neuromorphic event-based vision sensor. Learning relies on the combination of spike timing-dependent plasticity with different synaptic delays, the homeostatic regulations of synaptic weights and firing thresholds, and fast inhibition among neurons to decorrelate their responses. Our network develops biologically plausible spatio-temporal receptive fields when trained on real world input and is suited for implementation on neuromorphic hardware